Energy Savings Research Articles

Cfd-based optimization of Direct evaporative cooling systems for canarian greenhouses in Semi-Arid regions

Direct Evaporative Cooling Systems (DECS) offer a promising solution for mitigating heat stress in greenhouses located in arid and semi-arid regins. To implement DECS efficiently in these regions, it is crucial to understand their impact on greenhouse microclimate parameters. This study investigates the influence of DECS on the microclimate within a canarian greenhouse and identifies key factors affecting their efficiency. Through Computational Fluid Dynamics (CFD) simulations using Ansys, we examined the effects of DECS on temperature, relative humidity, and air velocity within the greenhouse under semi-arid conditions.The results indicate that DECS efficiency is affected by several factors, including fan placement, evaporative pad height, fan spacing, and inlet air fan speed. Higher fan placement cools the upper canopy but increases humidity near the ceiling, whereas lower placement improves temperature uniformity but may require additional humidification. Closer fan spacing results in uneven air velocity, while wider spacing leads to stagnant zones within the greenhouse. Optimal placement of the the evaporative pad is crucial for maximizing the cooling coverage area and minimizes the temperature gradients. Inlet air fan speed also significantly impacts the microclimate, with higher speeds reducing both temperature and humidity. By optimizing these parameters, the DECS system was able to significantly decrease air temperature, increase humidity, and improve air velocity inside the greenhouse. With a pad height of 1.5 m, fans positioned at a height of 2 m, a distance of 6 m between each fan, and an air speed of 1.5 m/s at the entrance – the DECS enhances the local climate. Under these conditions, the mean air temperature drops from 40°C −in greenhouse with natural ventilation- to 21 °C using DECS, while relative humidity increases from 23 % −in greenhouse with natural ventilation- to 67 % using DECS, creating a more favorable environment for plant growth and potentially increasing crop yield and quality. Additionally, energy and environmental analyses show that implementing DECS in Canarian greenhouses, instead of traditional electrical cooling systems, results in substantial energy savings of 123,818.112 kWh/year annually and reduces carbon dioxide emissions by approximately 1.785 tons/year over a 20-year operational lifespan in a semi-arid climate.

Soft Switching Technique in a Modified SEPIC Converter with MPPT using Cuckoo Search Algorithm

: MPPT refers to the process of continuously tracking and adjusting the operating point of a photovoltaic (PV) system to maximize the power output from the solar panels. The operating point at which a PV system produces the most power under a specific set of environmental circumstances is known as the maximum power point (MPP). The Maximum power point tracking (MPPT) technique is used to overcome the challenges of PV arrays with variable irradiance levels, which collects the greatest power from the PV array. Standalone PV systems, as well as gridconnected systems, can benefit from a DC-DC converter with MPPT. This study compares the standard perturbation and observation (P&O) technique with the soft-switching method used in the SEPIC converter utilizing the cuckoo search (CS) algorithm using MATLAB/Simulink. Reviewing the simulation results and assessing the SEPIC converter's performance. Background: SEPIC soft-switching converter, the soft-switching technique is applied to the SEPIC converter topology. It typically uses additional components, such as inductor and capacitors, to enable soft-switching operation. These additional components help to control the voltage and current waveforms across the main switching devices, reducing switching losses. Methods: The proposed method uses a soft switching technique to reduce switching losses and to improve efficiency. Soft switching is used in SEPIC (Single Ended Primary Inductor Converter) converters, a type of DC-DC converter, to enhance their performance. The projected solution further addresses the maximum power point tracking (MPPT) issue in PV systems using the Cuckoo Search (CS) method. Results: Soft switching technique is implemented in SEPIC converters to reduce these switching losses. In this system, Zero Voltage Switching (ZVS) involves turning on the switch when the voltage across it is zero. This allows the current to flow through the switch without creating any significant switching losses. CS algorithm can track the MPP quickly and accurately, it exhibits less prone to oscillations, easily monitor the MPP under a variety of weather circumstances. However, it exhibits negligible oscillation in a steady state, which results in significant power savings. Conclusion: The modified SEPIC converter with a soft-switching MPPT cuckoo algorithm is used with a solar-powered system. A prototype comprising a solar panel, a SEPIC converter, a driver, and a controller circuit was created. A soft-switching SEPIC converter with an MPPT cuckoo algorithm for the PV system is implemented, as are the converter operation, converter analysis, and theoretical analysis, and a controller circuit is analysed. The PV system, MPPT controller with tracking algorithm, and PMSM load were used in the system simulation. A 60-watt SEPIC converter prototype is built, and the outcomes are also tested experimentally.

AI-driven decarbonization of buildings: Leveraging predictive analytics and automation for sustainable energy management

The decarbonization of buildings is a pivotal strategy in addressing global climate change, and artificial intelligence (AI) is increasingly recognized as a powerful tool in this process. This paper explores the application of AI in optimizing building energy consumption, focusing on predictive analytics, real-time energy monitoring, and smart energy systems. By leveraging machine learning algorithms, AI enhances energy efficiency through precise automation, particularly in heating, ventilation, and air conditioning (HVAC) systems. AI-powered demand response solutions dynamically adjust energy use based on factors like occupancy, weather patterns, and energy prices, further minimizing carbon footprints. Additionally, the integration of renewable energy sources such as solar and wind with AI-driven energy management systems enables smarter utilization of clean energy. The paper examines both new construction projects and the retrofitting of existing infrastructures, offering insights into AI’s role in reducing carbon emissions and improving energy sustainability. Case studies highlight successful implementations, underscoring AI’s ability to drive significant energy savings and carbon reduction. The study also discusses challenges such as data privacy, the high cost of AI technology, and the regulatory framework required for widespread adoption. The findings present AI as an essential component of the future of sustainable, carbon-neutral buildings.

Proof of Work with Random Selection (PoWR): An Energy Saving Consensus Algorithm with Proof of Work and the Random Selection Function

Bitcoin, which has been used for 13 years, has a role in transactions and investments as a major cryptocurrency. However, as the number of users increases, Bitcoin faces difficulties, such as scalability for transaction throughput and energy-consumption problems due to the concentration of the mining pool. When Bitcoin first started to come out, it began to develop gradually through the mining of individuals. Nevertheless, as the price of the cryptocurrency gradually climbed, large mining corporation groups entered the mining competition with integrated circuit (IC) chips. Consequently, the substantial increase in power consumption is raising concerns regarding energy expenditure. This paper confirms that the verifiable random selection consensus protocol based on proof of work facilitates a fair and efficient system, enabling the participation of numerous individual miners in the mining competition while counteracting the monopolization of the hash rate by large mining corporations, thereby preserving the decentralization of mining. The protocol demonstrates the potential to mitigate substantial energy consumption. Moreover, it embodies features that create barriers to the adoption of high-energy-consuming application-specific integrated circuit equipment, significantly diminishing the principal factors contributing to extensive power utilization.

Revolutionizing superhighway traffic board lighting with remote LED spotlights

In this paper, we present a groundbreaking approach to enhance the illumination of traffic boards along superhighways, addressing significant challenges associated with conventional lighting systems. Our innovative method revolves around the strategic placement of remote spotlights at the side of the roadway to illuminate traffic signs equipped with retroreflector film (RTRF). The essence of our approach lies in remote illumination, which requires meticulous adjustment of the divergence angle of the spotlights to match the size of the signs and their distance from the projection source. To achieve the desired spotlight configuration, we have developed a hybrid optical system that incorporates a paraboloid reflector and a lens mounted on a bridge holder situated on top of the mirror. Through spot light illumination, we discovered that the initial divergence angle of the RTRF was too narrow. To improve projection angle tolerance, we recommend attaching a light diffuser film onto the surface of the RTRF. The coverage area ratio of the diffuser film can be adjusted to select the desired divergence angle for the reflected light. Our experimental measurements have yielded significant results, showcasing the half-divergent angle of the RTRF ranging from 3° to 7° for different coverage area ratios of the diffuser. In practical terms, with a target luminance of 300 nits at the white word on the traffic board, the power consumption of the spotlight fixture of the roadway was only 40 W, representing over 75% power savings when compared to traditional lighting methods. Consequently, our approach opts to utilize spotlights for illuminating specific traffic boards on superhighways, offering a more efficient and manageable lighting solution that greatly benefits both motorists and road maintenance personnel.

Phase change materials to improve the energy savings of wood building envelopes in Quebec

Phase change materials to improve the energy savings of wood building envelopes in Quebec

Performance Analysis of Spin Orbit Torque Magneto-Resistive RAM Caches in 4-core ARM Systems

Spin Orbit Torque Magnetic Random Access Memory (SOT–)MRAM is gaining interest as it eradicates several limitations posed by its predecessor Spin Transfer Torque (STT-)MRAM, yet inherits all its advantages. This work explores in detail, the suitability of SOT–MRAM implemented caches in different levels of memory hierarchy in comparison to conventional SRAM technology, over several performance parameters like area, energy consumption and execution time for an embedded benchmark suite. Our circuit-level analysis shows that SOT–MRAM outperforms SRAM for caches ([Formula: see text] KB), and only lags in area and read-access energy for smaller caches. A typical 512 KB SOT–MRAM cache improves area by 1%, read/write latency by 33/38%, and leakage by over 99% than that of SRAM memory technology. The architecture-level analysis confirms that on average SOT–MRAM is energy efficient by 74% in L1, 97.2% in L2 and 89.3% in both (i.e., L1 + L2) implementations against SRAM, for a 22[Formula: see text]nm technology node. We also estimate that SOT–MRAM only solution offers [Formula: see text]% energy savings and [Formula: see text]% better EDP than Hybrid (L1-SRAM and L2-SOT) memory hierarchy for multi-core ARM processors.

Web Server Based Electrical Control System Analysis for Smart Buildings

Energy management in smart buildings still faces challenges in optimizing energy use, particularly for high-load devices, such as HVAC systems and lighting. Conventional control systems are often inadequate for optimizing energy usage based on the operational needs of the building. This study aims to develop and analyze a web server-based electrical energy control system that can be accessed in real time to improve the energy efficiency of smart buildings. This study employed a quasi-experimental method by implementing a web server-based control system in a smart building and comparing the energy consumption before and after the application of the system. The results show that the system reduces the energy consumption by up to 25%, particularly for HVAC systems and lighting. The most significant energy savings occurred during off-peak hours, when the system automatically reduced power for unnecessary devices. The implications of this research suggest that a web server-based control system not only enhances energy efficiency and reduces operational costs, but also provides greater flexibility in energy management through more adaptive and responsive remote control. This research contributes to the development of more sustainable energy management technologies for smart buildings, with wide potential applications in commercial and institutional building scenarios

Artificial Neural Network to Predict Curvature Light Shelf Design Related Daylighting Optimization on Office Spaces

Energy Optimization in building design field now has been revolutionized due to AI and machine learning applications. Leveraging daylight to reduce artificial lighting consumption holds promise for significant energy savings, yet the nonlinear nature of daylight patterns poses challenges in prediction and optimization. This study proposes a novel approach to automated light shelf design using machine learning algorithms, specifically artificial neural networks (ANNs) such as recurrent neural networks (RNN) by long short term memory (LSTM), to accelerate daylighting simulation and optimization processes. The methodology employed two distinct approaches: Firstly, we employed the theoretical-analytical approach to explore methods for utilizing machine learning in natural lighting and light shelf parameters. Second, the practical and applied method involved creating a predictive model for designing the light shelf using appropriate AI and ML techniques. This model is based on an office geometry model at the El Arish weather file in Egypt, four-dimensional training room models with three internal zones-oriented south. Rhinoceros and Grasshopper, two parametric simulation tools, are used to normalize and optimize light shelf parameters like geometry cross-section, curvature surface, width, height position, depth, tilt angle, and reflectance materials. Then, the Galapagos plug-in and Colibri2 are used for dataset creation and optimization. The results demonstrate that automated light shelf operation has a significant impact on internal daylighting quality. RNNs enable rapid prediction of optimization models, reducing time consumption in the early design phase. ML facilitates decision-making by generating evaluative criteria from user-selected design options. RNNs were classified as good and bad and used LSTM to enhance prediction accuracy for efficient illuminance values metric in zones 1 and 2. Challenges include increasing the simulation procedure's efficiency. The results of model accuracy reached 99%. Hence, future research should prioritize resolving the previously identified concerns. In conclusion, this study underscores the importance of ML-driven approaches in early design phases to optimize building energy consumption and pave the way for sustainable architectural practices.

A multi-objective indirect neural adaptive processes control design for minimization of energy consumption: An experimental validation on a transesterification reactor

Nowadays, energy management environment is a very important issue that technologies have focused on in order to save costs and minimize energy waste. This objective can be achieved by means of an energy resource management approach through an appropriate optimization technique. However, energy savings can conflict with other objective functions, to solve the problem a multi-objective optimization that considers the minimization of the control energy can be adopted. In this paper, we propose to use a multi-objective indirect neural adaptive control concept for nonlinear systems with unknown dynamics. The control scheme consists of an adaptive instantaneous neural emulator and neural controller built on fully connected real-time recurrent learning networks (RTRL). The multi-objective particle swarm optimization (MOPSO) algorithm is used as a mechanism to find NE and NC adaptive learning rates that optimize the proposed objective functions: control energy minimization and closed-loop preservation. Comparative studies and experimental validation on a semi-batch reactor, used to generate cleaner biofuels, are performed to confirm the effectiveness of the proposed strategy.

The Regulation of Temperature Fluctuations and Energy Consumption in Buildings Using Phase Change Material–Gypsum Boards in Summer

This study examined the thermal performance of Comfortboard23, a commercial gypsum board from Knauf infused with phase change material (PCM). Structural characterization using XRD and SEM confirmed the presence of microencapsulated PCM within the gypsum matrix. The study does not provide a direct comparison between Comfortboard23 and other PCM-integrated gypsum boards on the market. This is a limitation of the research. A comprehensive comparison would involve testing multiple products under identical conditions, and evaluating factors such as thermal performance, cost-effectiveness, durability, and ease of installation. Thermal characterization involved a novel low-scale thermal chamber to measure U-value, thermal conductivity, heat storage capacity, and dynamic thermal response. Results showed incorporating PCM decreased the U-value by 2% compared to standard gypsum boards. Additionally, PCM inclusion increased heat storage capacity by around 45% and improved dynamic thermal characteristics by decreasing thermal stability coefficient from 0.92 to 0.76 and increasing thermal lag from 0.27 to 0.49 h. The 45% increase in heat storage capacity of Comfortboard23 could lead to a 10–20% reduction in heating and cooling energy consumption, improved thermal comfort, and potential HVAC downsizing. Exact benefits depend on climate, building design, and occupancy patterns, necessitating further research in diverse real-world settings. The findings demonstrate Comfortboard23’s potential for enhancing thermal energy storage in buildings, contributing to energy savings, improved thermal comfort, and reduced temperature fluctuations across varying daily temperatures. Overall, the study highlights the promise of Comfortboard23 as an energy-efficient PCM-integrated building material.

Innovative passive techniques and PCM integration in building envelopes for enhanced energy efficiency in desert regions

Rising energy demands in buildings for heating, cooling and operating appliances underscore the importance of implementing energy-efficient solutions. Passive cooling techniques provide a sustainable alternative to conventional, energy-intensive cooling systems, promising reduced energy consumption in buildings. Using a dynamic heat transfer model validated with ANSYS Fluent, this study examined three configurations: integrating phase change materials at the brick's centerline with cavities filled with air, EPS insulation, and emissivity coating. Additionally, the benefits of combining all three passive methods were evaluated. Results indicated that among the evaluated PCMs, capric acid demonstrated superior performance. While each passive method studied offers distinct advantages in enhancing thermal performance and achieving energy efficiency, assessing their combined effects revealed significant insights. Specifically, integrating capric acid at the brick's centerline, while simultaneously configuring EPS insulation in the upper cavities and emissivity coating in the lower cavities, yielded superior performance. This configuration significantly reduced inner surface temperature by approximately 6.6 °C and minimized heat flux by about 58.4 % compared to traditional bricks. Achieving energy savings of 56.24 %, this configuration shows potential to enhance both thermal comfort and energy efficiency in building applications.

Enhancing shipboard waste heat management with advanced technologies

The complexity of the energy systems onboard ships, combined with the different operating/weather conditions and the availability of cutting-edge technologies, makes analyses for improving the energy and environmental performances of ships time consuming and challenging. From this point of view, this article provides new criteria for the sustainable design and management of energy systems of existing or new ships. In particular, the impact of the adoption of organic Rankine cycle units, wet steam volumetric expanders, and single or double effect absorption chillers is here investigated. Two types of ships are examined as suitable case studies, evaluating the impact of each technology and their combinations by varying the shipping cruises. By using a dynamic simulation approach, potential savings and optimal solutions are assessed for different energy system layouts by also comparing their economic, energy and environmental impact performance. Results are reported in specific performance matrices for helping stakeholders in preliminary energy efficiency analyses.In particular, outcomes show that high cooling demands of ships in the Caribbean Sea enable primary energy savings close to 4.5 %, compared to 3.5 % in the Mediterranean Sea and 3 % in the North Sea. In the latter case, cooling needs can be almost fully balanced through the examined energy recovery technologies. Screw expanders integrate best in all operating conditions with short paybacks, whilst organic Rankine cycles (with electrical efficiency above 8%) are advantageous especially in cold climate routes. Benefits on environmental impact are significant, with avoided CO2 emissions around 6 kt/y, depending on the selected ship cruise.

Exploratory Analysis of a Novel Modular Green Wall’s Impact on Indoor Temperature and Energy Consumption in Residential Buildings: A Case Study from Belgium

One possible solution that mitigates the effects of climate change is the implementation of vertical greenery systems, which have the potential to reduce the need for cooling and provide energy savings for heating. This paper evaluates the effects of an innovative modular green wall on indoor temperature and energy use in a residential case study building. This research was carried out on a residential house in the city of Ghent, Belgium, whose southwest facade is covered with a specific type of modular green wall (a structure with a specific substrate and plants that have the ability to purify water so that it can be reused in the house). The monitoring process included four different temperatures (in front of and behind the green wall, in the substrate, and on the wall without greenery) during winter and summer periods. To analyze the effect on the internal temperature and energy use, a DesignBuilder simulation model was built and validated against these experimental results. This green wall has proven to have the greatest effect during the hottest summer days by reducing the indoor temperature by up to 3.5 °C. It also effectively increases the indoor temperature by up to 1.4 °C on a cold winter day, leading to energy savings of 6% on an annual basis.

Cooling Effectiveness of the Sustainable Cooling Solution for Cattle: Case Study in Poland

Recently, the dairy sector has been ever more affected by global warming. This study aimed to test a novel conductive cooling system for cattle that was successfully implemented and evaluated under summer thermally challenging weather conditions in Poland. The system consists mainly of the chiller, tank, and chilled water-driven mattress, designed to prioritize animal well-being. The experimental evaluation was carried out on three Friesian dry cows, housed on different types of bedding—commercial water mattress, straw, and cooling water mattress—and supplied with water at 10 °C (day) and 16 °C (night). The cooling water mattress’ surface temperature was twice as low as that of the commercial water mattress. The animal’s thermal comfort was assessed with physiological and behavioral reactions. The cooling effect on animals’ bodies was demonstrated with a lower reticulorumen temperature of the cooled cow (p < 0.05) than the reference ones. The local effect of cooling was proved with an 8 °C-lower skin temperature after the cow’s resting period. The presented study opens a new research direction toward dairy cattle’s welfare, sustainability, and the food–energy–water nexus, based on potential energy and water savings.

Assessing the Influence of Occupancy Factors on Energy Performance in US Small Office Buildings

Office buildings are responsible for about 35% of the total electricity in the US and over 70% of building energy consumption occurs during occupancy periods. Therefore, understanding occupancy behavior is crucial for reducing building energy consumption. However, given the stochastic nature of occupant behavior, identifying which occupancy parameters have the most impact on energy consumption poses a considerable challenge. This study aims to investigate and quantify the impact of various occupancy parameters on the energy performance of a US small-sized office building using an EnergyPlus-based nationwide energy simulation. First, dynamic occupancy schedules are created based on different occupancy parameters using an agent-based model. Next, the generated dynamic occupancy schedules are integrated into a small office building model from the Department of Energy’s prototypes. This creates a dataset of occupancy parameters and building energy performance across various climate zones. Finally, various feature selection and statistical analysis methods are applied to the generated dataset. This helps identify significant occupancy parameters and quantify their impact on building energy performance across different climate zones. According to the results of the study, buildings located in cool marine, mixed marine, and warm marine climate zones had lower total energy consumption compared to other zones. Additionally, feature selection methods identified “Occupant Density” as the primary significant variable impacting energy consumption, across all climate zones. These findings offer valuable insights into the influential occupancy parameters across various climate zones, highlighting the importance of tailoring occupancy schedules to enhance energy efficiency. They provide practical guidance that can be directly applied to optimize energy consumption and achieve significant energy savings in small office settings with different weather conditions.

Amino acids could sustain fungal life in the energy-limited anaerobic sediments below the seafloor.

In the depths beneath the ocean floor, where darkness, anaerobic conditions, and energy scarcity prevail, life persists against all odds. This study illuminates the pivotal role of amino acids, the fundamental building blocks of life, as a vital energy for deep subseafloor fungi. Our research uncovers how these fungi not only rely on amino acids for survival but also utilize them to reproduce, forming fruit bodies in environments deprived of oxygen and energy. This revelation not only elucidates the mechanisms enabling fungal survival in extreme conditions but also hints at the essentiality of amino acids as nutrients for other deep-sea microbes. By unraveling these mysteries of the hidden biosphere, our study opens new frontiers in understanding the resilience and adaptation of life in the most inhospitable environments on our planet.

Optimising Energy Efficiency in Offshore Buildings: Supporting SDG 7 - Clean and Affordable Energy

The offshore oil and gas sector faces significant challenges in reducing its carbon footprint while meeting global energy demand. Offshore structures consume up to 500 MW of energy per day. Despite the potential for 30-40% energy savings with efficiency technologies, only 25% of global platforms implement integrated energy management systems. This study aims to optimize energy efficiency in offshore structures to support SDG 7 - Affordable and Clean Energy. The study uses a multi-method approach, analyzing data from 10 offshore platforms, conducting energy modeling, and cost-benefit analysis. The results show variability in daily energy consumption (423.7 ± 32.5 MW) with key factors such as hydrocarbon production and environmental conditions. Energy efficiency technologies such as hybrid power generation systems and Integrated Energy Management Systems (SMET) show significant potential for savings. The optimization model shows a total potential energy savings of 37.2 ± 2.1%. Economic analysis using Monte Carlo simulations confirms the feasibility of investment with positive NPV for all evaluated technologies. SMET has the most favorable risk-return profile with a mean NPV of 5.8 ± 0.7 million USD. These findings provide important insights for operators and policymakers, emphasizing the importance of a holistic approach to energy management in offshore structures. Implementation of the proposed energy efficiency strategies can contribute significantly to climate change mitigation and the achievement of SDG 7.

Utility optimization for computation offloading and splitting in time-varying HAP and LEO satellite integrated MEC networks

To provide ubiquitous and low-latency communication and computation services for remote and disaster areas, high altitude platform (HAP) and low earth orbit (LEO) satellite integrated multi-access edge computing (HLS-MEC) networks have emerged as a promising solution. However, most current studies directly assume that the number of connected satellites is fixed and neglect the modeling of the time-varying multi-satellite computing process. Motivated by this, we establish an M/G/K(t) queuing model to illustrate task computation on satellites. To evaluate the efficiency and quality of computation offloading and splitting, we develop a utility model. This model is defined as a difference between a value function that assesses the trade-offs of task offloading, considering latency reductions and energy savings, and a cost function that quantifies expenses related to latency and energy consumption. After formulating the utility maximization problem, we propose the deep reinforcement learning-based offloading and splitting (DBOS) scheme that can overcome the time-varying uncertainties and high dynamics in the HLS-MEC network. Specifically, the DBOS scheme can learn the best computation offloading and splitting policy to maximize the utility by sensing the number of connected satellites, the distance between the HAP and satellites, the available computing resources, and the task arrival rate. Finally, we evaluate and validate the computational complexity and convergence property of the DBOS scheme. Numerical results show that the DBOS scheme outperforms the other three benchmarks and maximizes the utility under time-varying dynamics.

Revolutionizing Textile Manufacturing: Sustainable and Profitable Production by Integrating Industry 4.0, Activity-Based Costing, and the Theory of Constraints

The textile industry, a cornerstone of daily life and a highly globalized sector, faces significant environmental challenges due to its high water and energy consumption and extensive chemical usage. This study proposes a comprehensive green production planning and control model integrating Industry 4.0 concepts, activity-based costing (ABC), and the theory of constraints (TOC). The model utilizes mathematical programming to optimize product mix, maximize profitability, and minimize environmental impact. It leverages real-time sensing technologies and ERP systems to facilitate waste recovery, reduce carbon emissions, and achieve energy savings. Various carbon emission cost models, including continuous and discontinuous tax functions, are explored to balance corporate profitability with environmental sustainability. The findings demonstrate the model’s potential in optimizing resource utilization, reducing the environmental footprint, and enhancing profitability.